This open and free introductory statistics textbook covers topics typical for a college-level non-math majors statistics course. Topics include distributions, probability, research design, estimation, hypothesis testing, power and effect size, comparison of means, regression, analysis of variance (ANOVA), transformations, chi square, and non-parametric (distribution-free) tests). It is available as a pdf, online, or as an epub. An Instructor's Manual and PowerPoint slides are also available upon request from the project leader at Rice University.

A free and open introductory statistics textbook for non-math majors. "They have sought to present only the core concepts and use a wide-ranging set of exercises for each concept to drive comprehension. [...] a smaller and less intimidating textbook that trades some extended and unnecessary topics for a better-focused presentation of the central material." It covers descriptive statistics, probability, distributions, discrete and continuous random variables, estimation, hypothesis testing, comparison of means, correlation and regression, chi square, and F-tests.

"We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from interesting, real data. (3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world." This free and open introductory statistics textbook for non-math majors discusses data and data collection, foundations for inference with randomization and simulations (then leading into standard parametric statistics), inference with categorical and numerical data, and linear, multiple logistic regression. An introduction to probability is included as an appendix.

Curates multiple openly available statistics textbooks.

"Reference tool covering statistics, probability theory, biostatistics, quality control, and economics with emphasis in applications of statistical methods in sociology, engineering, computer science, biomedicine, psychology, survey methodology, and other client disciplines." A good source for topics less often covered in the general textbooks.

"More than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions." Another good resource for topics not included in the general texts listed previously.

"designed to address some of the most frequently asked questions about ordination. It is my intention to gear this page towards the student and the practitioner rather than the ordination specialist"

"Principal component analysis (PCA) simplifies the complexity in high-dimensional data while retaining trends and patterns. It does this by transforming the data into fewer dimensions, which act as summaries of features. "

"The rationale for this method, the math under the hood, some best practices, and potential drawbacks to the method."

"A Multivariate Analysis problem could start out with a substantial number of correlated variables. Principal Component Analysis is a dimension-reduction tool that can be used advantageously in such situations. Principal component analysis aims at reducing a large set of variables to a small set that still contains most of the information in the large set. "

"This how-to guide covers the mechanics of data collection for applied qualitative research. It is appropriate for novice and experienced researchers alike. It can be used as both a training tool and a daily reference manual for field team members. The question and answer format and modular design make it easy for readers to find information on a particular topic quickly."